展示HN：Empromptu.ai – 自主型人工智能构建人工智能应用程序
嘿，HN！我们是Empromptu.ai，一款构建AI应用的工具（每个应用内都内置了RAG、模型和评估功能）。
演示链接： [https://app.storylane.io/share/rtneodkf5i1l](https://app.storylane.io/share/rtneodkf5i1l)
我们创建Empromptu是因为在使用其他AI构建工具时消耗了成千上万的积分，却遇到了同样的问题：看起来很酷的原型或演示在真实用户面前崩溃。
问题不在于构建过程，而在于准确性。大多数AI应用的可靠性停留在60%左右，这对于原型来说可以，但在生产环境中是不可用的。我们意识到这些工具并不是真正的“AI应用构建器”，而是恰好使用了AI的网站构建器。
我们想先解决最棘手的问题：让AI应用实际可靠地工作。
我们的方法集中在我们称之为动态优化的技术上。我们的系统不是将每个可能的场景塞入一个庞大的提示中（这会让大型语言模型感到困惑），而是根据上下文进行调整。例如，一个旅行聊天机器人会自动知道在提到洛杉矶时要提到LAX，而提到多伦多时要提到Pearson。这种方法的准确率稳定在90%左右，而行业标准仅为60%左右。
但仅有准确性还不够，因为我们还需要解决构建者的差距：
- 简单构建器（如Lovable、Bolt）：创建静态网站，而不是AI应用。
- 复杂的机器学习工具：需要专门的团队，而大多数初创公司并没有（如Arize、Voxel51）——我们也听到过技术和非技术创始人表示这些工具非常复杂。
- 缺失的部分：构建应用的工具，其中AI是嵌入的功能。
因此，我们构建了内置优化的AI代理。用户只需输入他们想要构建的内容，我们的代理就会处理完整的开发流程：创建嵌入模型、RAG和智能处理的应用。您可以通过Netlify、GitHub部署到自己的基础设施，或直接下载，因为您可以在本地运行它。
结果是：初创公司、独立开发者和企业可以在不雇佣专门的机器学习团队的情况下构建AI应用或AI功能。
候补名单： [https://empromptu.ai](https://empromptu.ai)
我们非常希望听到HN社区的反馈——特别是如果您遇到过类似的准确性问题或对技术方法的看法。
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Hey HN! We're Empromptu.ai, an AI app builder that builds AI apps (RAG, models, evals all built in every app)<p>Demo: <a href="https://app.storylane.io/share/rtneodkf5i1l">https://app.storylane.io/share/rtneodkf5i1l</a><p>We started Empromptu after burning through thousands of credits on AI builders and hitting the same problem: cool looking prototypes or demos that break with real users.<p>The issue wasn't the building process, it was accuracy. Most AI applications plateau at 60%~ reliability, which is fine for prototypes but it's unusable in production. We realized these tools aren't really "AI app builders", they're website builders that happen to use AI.<p>We wanted to solve the hardest problem first: making AI applications actually work reliably.<p>Our approach centers on what we call dynamic optimization. Instead of cramming every possible scenario into one massive prompt (which confuses LLMs), our system adapts contextually. A travel chatbot automatically knows to mention LAX for Los Angeles vs. Pearson for Toronto. This consistently delivers 90%~ accuracy versus the industry standard 60%~.<p>But accuracy alone wasn't enough because we also needed to solve the builder gap:<p>- Simple builders (Lovable, Bolt): Create static websites, not AI applications<p>- Complex ML tools: Require dedicated teams most startups don't have (Arize, Voxel51) - we've also heard from both technical and non-technical founders that they found these tools very complex<p>- What's missing: Tools that build applications where AI is embedded functionality<p>So we built AI agents with optimization built-in. Users just type what they want to build and our agents handle the full development pipeline: creating applications with embedded models, RAG and intelligent processing. You can deploy to your own infrastructure via Netlify, GitHub or download it directly since you can run it locally.<p>The result: Startups, solo hackers and enterprises can build AI apps or AI features without hiring a dedicated ML team.<p>Waitlist: <a href="https://empromptu.ai" rel="nofollow">https://empromptu.ai</a><p>We'd love feedback from the HN community — esp. if you've hit similar accuracy problems or thoughts on the technical approach.